DEEMD: Drug Efficacy Estimation against SARS-CoV-2 based on cell
Morphology with Deep multiple instance learning
Abstract
Drug repurposing can accelerate the identification of effective
compounds for clinical use against SARS-CoV-2, with the advantage of
pre-existing clinical safety data and an established supply chain. RNA
viruses such as SARS-CoV-2 manipulate cellular pathways and induce
reorganization of subcellular structures to support their life cycle.
These morphological changes can be quantified using bioimaging
techniques. In this work, we developed DEEMD: a computational pipeline
using deep neural network models within a multiple instance learning
framework, to identify putative treatments effective against SARS-CoV-2
based on morphological analysis of the publicly available RxRx19a
dataset. This dataset consists of fluorescence microscopy images of
SARS-CoV-2 non-infected cells and infected cells, with and without drug
treatment. DEEMD first extracts discriminative morphological features to
generate cell morphological profiles from the non-infected and infected
cells. These morphological profiles are then used in a statistical model
to estimate the applied treatment efficacy on infected cells based on
similarities to non-infected cells. DEEMD is capable of localizing
infected cells via weak supervision without any expensive pixel-level
annotations. DEEMD identifies known SARS-CoV-2 inhibitors, such as
Remdesivir and Aloxistatin. supporting the validity of our approach.
DEEMD can be explored for use on other emerging viruses and datasets to
rapidly identify candidate antiviral treatments in the future. Our
implementation is available online at
https://github.com/Sadegh-Saberian/DEEMD.